accuracy assessment of modis fire products in african savanna...
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University of Zimbabwe
Accuracy assessment of MODIS fire
products in African Savanna woodlands.
A Dissertation submitted to the Department of Geography and
Environmental Science in Partial Fulfilment of the Requirements for
the Master of Science Degree in Geographical Information Science
and Remote Sensing, June 2016
NDUMEZULU T MPOFU
R156317D
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Acknowledgements
My greatest challenge was moving from a social science back ground into a purely scientific
line of thinking yet, I consider it a privilege to have ventured into this pathway. Despite all the
hurdles I had to jump all the way I learnt eventually that, “where there is a will, there surely is
a way”. I therefore start by expressing my sincere gratitude to my family for all the support
they gave during the course of my Master of Science study.
Let me begin by acknowledging the contributions of my thesis supervisor Doctor
Mhosisi Masocha, who helped me realise my potential and develop a scientific, independent,
smart and hardworking mind. To Masocha, I am not able to thank you enough for helping me
realise the scope of my study and perfect its rationale. I thank you most for the times when you
pushed me to work smart and discover things on my own, it would be very ungrateful of me
not to admit the positive difference it made in me. I am grateful I had you for my mentor. You
taught me a lot, from the basic GI science to scientific writing, appreciation and ‘yes!’, I will
always remember to limit the content in my slides when I teach somewhere else or make
presentations out there in the world.
To Henry Ndaimani, your encouragement, motivation and assistance during the course
of my thesis, I appreciate with overwhelming gratitude.
To my classmates, I thank you all for all the contributions you made when things where
a bit hazy on my end. The spirit we had as a team, may it persist even out there in the world.
Finally, chief of all, I thank God for providing for me continually, life, strength, courage and
dedication throughout the course of my study. My faith in you will abide forever. Amen
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Abstract
Fire poses a continuous threat to forest ecosystems and can dramatically reduce valuable timber
species in forest woodland areas. The occurrence of fire in Baikiaea plurijuga woodlands
warrants the need of an active and timely fire detection system. Rudimentary methods of
detecting fires are still in use in most of Zimbabwe’s forest reserves, yet remote sensing has
played a pivotal role around the globe in detecting and monitoring both active incidences and
post fire burnt areas. Several satellite systems have been validated in different biomes of the
world for both MODIS MOD14A1 and MCD14ML.However, there still remains a gap of
knowledge in the accurate detection of fires in Baikiaea plurijuga woodlands. In this study we
evaluate the accuracy of MODIS fire products using, confusion matrices, kappa statistic, true
skill statistic (TSS), remote sensing and Geographical Information techniques where employed
to assess the accuracy of MODIS MOD14A1 burnt area product and MCD14ML active fire
product in fire detection. This is the first time results of accuracy assessment of fire products
are reported in Baikiaea plurijuga woodlands. In both study sites, course resolution 1 km
MODIS MOD14A1 burnt area fire product has a continually poor index of agreement with
ground data kappa 0 and TSS value is 0. However, for MODIS MCD14ML we found high
kappa and true skill statistic values, showing a high. However, we recorded high kappa and
TSS values for the MODIS MCDML active fire product. These results, are consistent with the
premise that, increase in spatial resolution reduces the sensors ability to detect fires in African
Savanna woodlands.
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Table of contents
Acknowledgements ..................................................................................................................... i
Abstract ...................................................................................................................................... ii
Table of contents ...................................................................................................................... iii
List of Figures ........................................................................................................................... iv
List of Appendices .................................................................................................................... vi
List of Tables ............................................................................................................................ vi
List of Abbreviations and Acronyms ....................................................................................... vii
Chapter 1: Introduction .............................................................................................................. 1
1.1: Problem statement........................................................................................................... 3
1.2: Objectives ....................................................................................................................... 3
1.3: Hypothesis ...................................................................................................................... 3
1.4: Justification of the study ................................................................................................. 4
Chapter 2: Materials and methods ............................................................................................. 5
2.1: Gwayi State Forest .......................................................................................................... 5
2.1.2: Matusadona National Park ....................................................................................... 7
2.2: Data Collection ............................................................................................................... 8
2.3: Data Analysis ................................................................................................................ 11
2.3.1: Pre-processing and Image analysis ........................................................................ 11
2.3.2: Post Classification...................................................................................................... 12
2.4: Statistics employed ................................................................................................... 12
Chapter 3: Results .................................................................................................................... 15
3.1: Validation of MODIS MOD14A1 in Gwayi State Forest ............................................ 15
3.2 Validation of MODIS MCD14ML ................................................................................ 15
3.3: Validation of MODIS MOD14A1 in Matusadona National Park ................................ 15
3.4: Validation of MODIS MCD14ML ............................................................................... 16
Chapter 4: Discussion .............................................................................................................. 29
Chapter 5: Conclusion.............................................................................................................. 31
Bibliography ............................................................................................................................ 33
Appendices ............................................................................................................................... 38
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List of Figures
Figure 1: Map showing the spatial distribution of Baikiaea plurijuga woodlands in Zimbabwe
and the location of Gwayi State forest. ...................................................................................... 6
Figure 2: Map showing fire points collected from the Forest Commission database for the years
2000 to 2009 ............................................................................................................................ 10
Figure 3 Landsat 5 false colour composite. ............................................................................. 10
Figure 4 Landsat 5 false colour composite ............................................................................. 11
Figure 5 Map showing agreement between ground fire points and the classified MOD14A1 fire
product for the year 2000. ........................................................................................................ 17
Figure 6 Map showing agreement between ground fire points and the classified MOD14A1 fire
product for the year 2005. ........................................................................................................ 18
Figure 7 Map showing agreement between ground fire points and the classified MOD14A1 fire
product for the year 2006. ........................................................................................................ 19
Figure 8 Map showing agreement between ground fire points and the classified MOD14A1 fire
product for the year 2009. ........................................................................................................ 20
Figure 9 Map showing agreement between ground fire points and the classified MCD14ML
fire product for the year 2000. ................................................................................................. 21
Figure 10 Map showing agreement between ground fire points and the classified MCD14ML
fire product for the year 2005. ................................................................................................. 22
Figure 11 Map showing agreement between ground fire points and the classified MCD14ML
fire product for the year 2006. ................................................................................................. 23
Figure 12 Map showing agreement between ground fire points and the classified MCD14ML
fire product for the year 2009. ................................................................................................. 24
Figure 13: Map showing agreement between ground fire data and the MOD14A1 fire product
for the year 2015 (day 1) .......................................................................................................... 25
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Figure 14: Map showing agreement between ground fire data and the MOD14A1 fire product
for the year 2015 (day 2) .......................................................................................................... 26
Figure 15: Map showing agreement between ground fire data and the MCD14ML fire product
for the year 2015 (day 1) .......................................................................................................... 27
Figure 16: Map showing agreement between ground fire data and the MOD14A1 fire product
for the year 2015 ...................................................................................................................... 28
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List of Appendices
Appendices 1: Error matrix tables for MODIS MOD14A1 in Gwayi Forest .......................... 38
Appendices 2: Error matrix tables for MODIS MCD14ML in Gwayi Forest ......................... 39
Appendices 3: Error matrix tables for MODIS MCD14ML in Matusadona National Park .... 39
Appendices 4: Error matrix tables for MODIS MOD14A1 in Matusadona National Park ..... 39
List of Tables
Table 1: Shows the two satellites validated in this study and their characteristics.................... 9
Table 2: Accuracy statistics for MODIS MOD14A1 and MODIS MCD14ML fire products in
Gwayi State forest in Zimbabwe.............................................................................................. 16
Table 3: Accuracy statistics for MODIS MOD14A1 and MODIS MCD14ML fire products in
Mutusadona National Park in Zimbabwe ................................................................................ 17
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List of Abbreviations and Acronyms
MODIS – Moderate Resolution Spectroradiometer
FIRMS – Fire Information for Resource Management
TSS – True Skill Statistic
ASTER – Advanced Spaceborne Thermal Emission and Reflection Radiometer
NOAA – National Oceanic and Atmospheric Administration
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Chapter 1: Introduction
Fire has the capacity to break sustainable environments and can dramatically reduce the living
biomass in tropical forests even to the same degree as logging activities (Weber and Flannigan,
1997). Fire can significantly affect biomass composition and content of tropical trees and the
composition of flora, which are not adapted to this disturbance (Martins et al., 2012).
Ecosystems subjected to, frequent and severe fire disturbance can offset this balance resulting
in the ecosystem’s loss of recovery ability and major ecological functions (Held, 2006).
Empirical studies have demonstrated the higher mortality of trees with smaller diameter in
areas which experienced recent burning (Stroppiana et al., 2003) . Whereas an increase in
mortality of larger trees (≥50 cm dbh) has been reported 1-3 years after a fire (Stroppiana et
al., 2003) attributed to both natural and anthropogenic causes. Africa is referred to as the “Fire
Continent” and biomass burning is recognised as an important and extensive function of
African grasslands and savannas. However, 168 million hectares burn annually and savanna
burning accounts for 50% of this total. In African savannas fire is a key component playing a
pivotal ecological role in controlling vegetation patterns (Houghton, 2007).
Satellite based remote sensing observations have been used to generate global-scale burned
area (BA) products providing estimates of the extent and severity of damages (Brink and Eva,
2009). Satellite remote sensing is an essential technology for gathering post-fire-related data in
an affordable and time conserving manner, given the extremely broad spatial expanse and often
limited accessibility of the areas affected by forest fire (Veraverbeke et al., 2012). The
assessment of fire regimes across broad areas is done competently using data obtained from
remote sensors such as Moderate Resolution Spectroradiometer, SPOT, and National Oceanic
and Atmospheric Administration fire products, which are specifically designed for this purpose
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(Korontzi et al., 2006) . Fire detection is performed using an algorithm that exploits the strong
emission of mid-infrared radiation from fires (Giglio et al., 1999). Burnt area detection is
critical for forest reserves to estimate, in automated manner, the aggregate expanse of the burnt
surface in time and space and is highly demanded for statistical inventories. Moreover, it is a
step towards more detailed analysis such as burn frequency and severity (Giglio et al., 2009).
Several studies, (Padilla et al., 2014) have validated different fire products in various parts and
different biomes of the world. Padilla et al., (2015) for example, compared the accuracies of
six remote sensing global burned area products using stratified random sampling and
estimation at a global scale in 2008. Padilla et al., (2014) carried out a performance study of
the MODIS MCD45A1 in automated burned area detection, in the Brazilian savanna.
Schroeder et al., (2008) validated GOES and MODIS active fire detection products using
ASTER and ETM+ data. A spatio-temporal analysis of fire relapse and magnitude for semi-
arid savanna ecosystems in Southern Africa was also done by (Pricope and Binford, 2012)
using moderate-resolution satellite imagery. In 2000 (Silva et al., 2003) used SPOT-
VEGETATION satellite data to estimate, during the dry seasons areas in Southern Africa. Silva
et al., (2003) carried out a study on burned area mapping in Greece using SPOT-4 HRVIR
images and Object-Based image analysis and suggested that, accurate information relating the
impact of fire environment is a key factor in, quantifying the impact of fires on landscapes,
selecting and prioritizing treatments applied on site, planning and monitoring restoration and
recovery activities and providing baseline information for future monitoring.
It is important to note that, not all satellites are efficient in detecting fire scars due to different
spatial, spectral and temporal resolutions. Empirical studies have established that, fire
comprises a major threat to protected areas and suggested that, a special focus be placed in the
detection of fire scars and rapid alert of fires within forest reserves. This study tested the
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performance of two MODIS fire products in detecting burnt area scars in Baikiaea plurijuga
woodlands that is, MODIS MOD14A1 and MODIS MCD14ML.
1.1: Problem statement
By measuring thermal anomalies caused by active fires, remote sensing has considerable
potential for mapping burnt areas and fire regimes. However, limited studies have focused in
evaluating the impact of sensor resolution in detecting burnt scars or active fires particularly in
African Savana woodlands. . Because of their hardwood nature, African Savanna woodlands
have been designated as an important economic resource and the incidence and severity of
forest fires appears to have accelerated over the past decade reducing the commercial value of
this timber resource.
1.2: Objectives
1. To test the performance of MODIS MOD14A1 and MCD14ML fire products in the
detection of fire scars within African Savanna woodlands.
2. To assess the effects of spatial resolution and sensor characteristics on ability of remote
sensing products to detect fire in African Savanna woodlands
1.3: Hypothesis
Increase in spatial resolution decreases the sensors ability to detect fire scars in African
Savanna woodlands.
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1.4: Justification of the study
Baikiaea plurijuga or teak woodlands, confined to the western and north western parts of the
country, on Kalahari sands, extending over one million hectares; contain the most
commercially exploitable indigenous timber species. Their average productivity ranges
between 150-200 m3/ha with a mean annual increment of 0.6 to 0.7 m3/ha (Chihambakwe,
1987). Because of their economic value, about 800 000 hectares of the natural baikiaea
woodlands were demarcated and gazetted as forest reserves. However, the incidence and
severity of forest fires appears to have accelerated over the past decade and reduces the
commercial value of timber (CHIHAMBAKWE, 1987)). Hence, accurate detection of fire in
these ecosystems using remote sensing and statistical indices of agreement may contribute
towards development of an operation fire detection system.
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Chapter 2: Materials and methods
2.1: Gwayi State Forest
In this study accuracy assessment of the two fire products was done in two different study sites
that is, Gwayi State forest and Matusadona National park. Gwayi forest is one of several
designated forest areas meant to preserve biodiversity in its various forms across the country
(Zimbabwe). Gwayi state forest covers an area of about 1430 km2 and is located in the west of
Zimbabwe between latitudes 18̊ 45ˈ and 19̊ 30ˈS and longitudes 27̊ 40ˈ and 28̊ 22ˈE (Figure
3.1). Gwayi forest falls in region IV and V. Natural Region IV is located in the low-lying areas
in the north and south of the country and is characterised by a mean annual rainfall of 450-650
mm, severe dry spells during the rainy season and frequent seasonal droughts. Natural Region
V covers the lowland areas below 900 m above sea level in both the north and south of the
country and the mean annual rainfall in this region is less than 650 mm and is highly erratic.
Gwayi state forest is often referred to as the ‘Kalahari Sand Forest’ (Matose, 2008). This is
because most of the forest is located on deep Kalahari sands, whilst a small portion consists of
varied soils derived from sandstones and grits, with strong influences of Basalt. Considerable
alluvial soil occurs in close proximity to the main river such as Gwayi River (Mudekwe, 2006).
The uneven topography, poor soils, short rainy seasons and droughts that occur make the area
unsuitable for crop production even drought resistant dry-land cropping is subject to risk and
uncertainty. In general, Gwayi forest, as an indigenous forest, is critical in the management and
protection of the fragile Kalahari sand ecosystem. For rivers feeding into Zambezi, Gwayi
forest serves as a an important shield to catchment area protection and biodiversity
conservation, a shelter to wild animals and as a foundation of commercial timber and non-
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timber forest products such as honey, mushrooms, edible insects and indigenous fruits (Matose,
2008).
The forest area was predominately designated for ensuring a sustained supply of hardwood
timber and revenue from timber concessions by the Forestry Commission (Field and Raupach,
2004). More so, Gwayi forest is particularly valuable for wildlife resource, as it contains the
complete range of ecosystems and an existing valuable wildlife population (Karsenty, 2008).
Grazing leases have gained importance to the Forest Commission's revenue generation system
since the 1990s and after wildlife related revenue and timber concessions, grazing leases are
the third highest source of revenue for the Gwayi Forest (Mudekwe, 2006).
Figure 1: Map showing the spatial distribution of Baikiaea plurijuga woodlands in
Zimbabwe and the location of Gwayi State forest.
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2.1.2: Matusadona National Park
One of the few existing nature reserves, once considered also, a haven for endangered Black
Rhino and lions in Africa is Matusadona National park located on Lake Kariba’s shore. (Taylor,
1986). West of the Ume River, east of Sanyati river an area of about 338 000 (Purchase and
Du Toit, 2000). The origin of its name dates back to the mid-1900’s and it was named after the
foot-high “Matuzviadonha” Hills, also known as the Zambezi escarpment, located west of the
park. The Open woodlands on the upland, in the rear of the escarpment, are dominated by
Julbernardia globiflora (Purchase and Vhurumuku, 2005). Common also, on the slopes and
edges of the escarpment are acacia, Brachystegia glaucescens, and thick patches of Jesse bush,
Mopane scrub and woodland. Lakeshore marks the entire northern boundary of the park, a
shoreline frequently subjected to irregular variations in water level as a result of fluctuations
in annual rainfall and still experiencing rapid ecological development and change (Purchase
and Du Toit, 2000).
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Figure 2. Map showing the location of Mutusadona National Park and ground fire points for
the year 2015.
2.2: Data Collection
Ground point data of burnt area scars was collected from the Forestry Commission Database
for the years 2000 to 2009 as shown in Figure 2 below. GPS fire data recorded in Matusadona
National park was provided by Doctor Masocha at University of Zimbabwe Geography and
Environmental Science Department. MODIS MOD14A1 1 km burnt area fire product and
MODIS MCD14ML 375 m active fire product for the years 2000 to 2009 where downloaded.
The selected Moderate Resolution Imaging Spectroradiometer (MODIS), has a I km spatial
resolution and a 12 hour return interval or temporal resolution. The MOD14A1 burnt area
product is based on the bidirectional reflectance distribution function (BRDF) modelling of the
surface, and detection of a persistent change in surface response, and primarily uses visible and
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shortwave infrared time series data (Roy et al., 2005). Since initiation in April 2001, the Rapid
Response System is providing MODIS fire data and imagery (Justice et al., 2000). The Fire
Information for Resource Management System (FIRMS) MCD14ML product provides data in
a flexible web GIS format (URI.3) at a spatial resolution of 375m and was developed to provide
near real-time data within a 2 to 4hour acquisition time interval (Justice et al., 2010). (Justice
et al., 2002) also stated that, the active fire algorithm uses multiple channels to detect thermal
anomalies on a per pixel basis and the algorithm uses brightness temperatures derived from the
MODIS 4 and 11 Am channels, denoted by T4 and T11, respectively.
Table 1: Sensor characteristics validated in this study.
Satellite Spatial
Resolution
Temporal
resolution
No. of Bands Product
MODIS
MOD14A1
1km 12hr 8 Burnt Area
MODIS
MCD14ML
375m 2-4hrs Active Fire
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Figure 2: Map showing fire points collected from the Forest Commission database for the
years 2000 to 2009
Figure 3 Landsat 5 false colour composite showing an overlay of ground fire data on burnt
areas for the year 2000 (left tile) and 2005 (right tile) in Gwayi State forest. The black border
marks the boundary of Gwayi State forest, fire in these images appears red and ground fire
points (for the respective years) used for validation appear yellow.
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Figure 4 Landsat 5 false colour composite showing an overlay of ground fire data on burnt
areas for the year 2006 (left tile) and 2009 (right tile). The black border marks the boundary
of Gwayi State forest, fire in these images appears red and ground fire points (for the
respective years) used for validation appear yellow.
2.3: Data Analysis
2.3.1: Pre-processing and Image analysis
MODIS MOD14A1 1km burnt area fire product and MODIS MCD14ML 375 m fire product
for the years 2000 to 2009 where downloaded from two remote sensing websites,
glovis.usgs.glov and FIRMS Alert. The two Modis fire products were downloaded with a
geographic coordinate system and projected to UTM coordinate system zone 35 South, were
the study site is located. Given the limited data obtained from the ground for cross validation,
only images for the years 2000, 2005, 2006 and 2009 where used to test the performance of the
afore mentioned fire products in detecting fire scars in Baikiaea plurijuga woodlands.
The slicing operator in ILIWIS 3.0 was used to classify the range of values of the MOD14A1
fire product into two classes, fire and none fire output map. A group domain was created
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beforehand listing the upper value boundaries of the groups and the class names. The classified
maps were imported into Arc GIS 10.1 and cells of the classified map that correspond to the
areas defined by the study area were masked out for further analysis.
2.3.2: Post Classification
We assessed the accuracy of the re-classified images using fire incidence data measured in the
field with a global position system (GPS) handheld receiver. In Gwayi, the location of active
fire scars was measured by forest officers employed by the Forest Commission. In Matusadona,
one of the authors measured location of fire incidence points. For each study site and date, we
overlaid a point map of burnt sites on top of raster layer for active fires obtained from MODIS,
FIRMS and SPOT the databases in Arc Map 10.1. We then used the extraction tool from the
arc tool box spatial analyst package to extract the cell values of each raster that is, the fire
products, based on the set of fire incident data measured in the field. We then used the
frequency tool from the statistics package to create a new table containing unique field values
and the number of occurrence of each unique field value, that is, for every different ground
point and every different predicted value, what the frequency is. Then we calculated error
matrices or pivot tables using results from the frequency tables.
2.4: Statistical Analysis
To summarise the relationship between satellite data and reference test data we used 2x2 error
matrices. From the error matrices overall accuracy, which incorporates the major diagonal and
excludes the omission and commission errors, was calculated summarising the total agreement
or disagreement between the maps (Allouche et al., 2006).
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We also employed Khat a kappa statistic which is a measure of agreement or accuracy. To
guarantee the accuracy of remote sensing derived data in fire detection, we tested both MODIS
burnt area and active fire products. The kappa statistic derived from error matrices, incorporates
the off-diagonals of the error matrices representing agreement obtained having removed the
proportion of agreement that could be expected to occur by chance. Kappa values Range from
-1 to +1, values > 0.80 present strong agreement, values between 0.4 and 0.8 represent
moderate agreement and Values < 0.4 are indicative of a poor agreement (Balogun and Salami,
2016).
𝐾 =Pr(𝑎) − Pr(𝑒)
1 − Pr(𝑒)
In this study we also used the true skill statistic, This statistic is a simple and intuitive measure
for the performance of distribution models when predictions are expressed as presence–absence
maps (Allouche et al., 2006). It is a skill score expressing the hit rate relative to the false alarm
rate and remains positive as long as H remains greater than F. For a 2x2 confusion matrix TSS
is defined as:
𝑇𝑆𝑆 =𝑎𝑑 − 𝑏𝑐
(𝑎 + 𝑐)(𝑏 + 𝑑)𝑜𝑟𝑆𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦 + 𝑆𝑝𝑒𝑐𝑖𝑓𝑖𝑐𝑖𝑡𝑦 − 1
It takes into account omission and commission errors, and success as random speculation.
Sensitivity is the proportion of observed presences that are predicted as such therefore, it
quantifies omission errors. Specificity is the proportion of observed absences that are predicted
as such therefore, it quantifies commission errors. TSS Values Range from -1 to +1. A value
of +1 indicates a perfect agreement and values less than or equal to 0 indicate a performance
significantly no better than random (Nüchel et al., 2015).
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Compared to kappa, TSS is not affected by prevalence, size of the validation set. In contrast to
kappa, documented effects of prevalence on TSS can be interpreted as evidence for real
ecological phenomena rather than statistical artefacts (Allouche et al., 2006).
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Chapter 3: Results
3.1: Validation of MODIS MOD14A1 in Gwayi State Forest
Results from kappa and true skill statistic for the year 2000 to 2009 suggest that, there is a poor
agreement between the MOD14A1 fire product and the ground fire points used as the test set
and, the sensors ability to detect fires is no better than random. All fire pixels have been
commissioned to the non-fire class resulting in low values of both kappa and true skill statistic
(Table 1). Figure 5, 6, 7 and 8 below show also that, there is no improvement in the products
ability to detect fires over the years under study.
3.2 Validation of MODIS MCD14ML
Accuracy statistics for MODIS MCD14ML suggest a strong agreement between the fire
product and ground fire data. With the exception of the year 2000 with low kappa and true skill
statistic values, 2005, 2006 and 2007 gave us values close to one for both kappa and true skill
statistic suggesting a performance far from random in detecting fires in Baikiaea Plurijuga
woodlands (Table 1). However, as shown in figure 10, 11 and 12, there are a few commission
and omission errors reducing the products overall accuracy in fire detection.
3.3: Validation of MODIS MOD14A1 in Matusadona National Park
Kappa and true skill statistic results show that there is a poor agreement between the fire
product and ground fire data collected in Matusadona national park. We found very low values
of kappa and TSS in both days that is, 18 September 2015 and the 20th of September of the
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same year. Figures 13 and 14 below confirm visually the disagreement between the product
and ground data.
3.4: Validation of MODIS MCD14ML
We found a moderate agreement between the MODIS MCD14ML active fire product and the
ground test data collected in Matusadona National Park. We also observed that, moderately
high values of kappa and the true skill statistic for 20 September 2015 and relatively values for
the 18th of September same year (Figure 15 and 16).
Table 2: Accuracy statistics for MODIS MOD14A1 and MODIS MCD14ML fire products in
Gwayi State forest in Zimbabwe.
Fire Product Year Overall
accuracy
Kappa Value TSS Value
MOD14A1 2000 0.5 0 0
2005 0.5 0 0
2006 0.5 0 0
2009 0.5 0 0
MCD14ML 2000 0.5 0 0
2005 0.8 0.6 0.6
2006 0.9 0.9 0.9
2009 0.8 0.7 0.7
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Table 3: Accuracy statistics for MODIS MOD14A1 and MODIS MCD14ML fire products in
Mutusadona National Park in Zimbabwe
Fire Product Day/Year Overall
Accuracy
Kappa Value TSS Value
MOD14A1 18 September
2015
0.4 0 0
20 September
2015
0.3 0 0
MCD14ML 18 September
2015
0.5 0.3 0.3
20 September
2015
0.7 0.6 0.6
Figure 5 Map showing agreement between ground fire points and the classified MOD14A1
fire product for the year 2000.
Figure 5 illustrates an overlay of ground data onto the MOD14A1 burnt area product for the
year 2000 in Gwayi State forest. The red points are fire data and the black surface is a clip of
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the MOD14A1 burnt area product clipped using Gwayi State forest boundary. It can be
observed that in this year the product did not detect any burnt areas at least within Gwayi and
the fire data is falling onto none fire pixels. This indicates a poor index of agreement between
ground fire and the burnt area product.
Figure 6 Map showing agreement between ground fire points and the classified MOD14A1
;fire product for the year 2005.
Figure 6 is showing that there is no improvement in agreement between the test set and the
burnt area product from the year 2000. Instead, a poor agreement between the two datasets can
still be observed for the year 2005. No burnt area scars can be seen in the MOD14A1 for this
year.
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Figure 7 Map showing agreement between ground fire points and the classified MOD14A1
fire product for the year 2006.
Figure 7 is showing a poor agreement between the test set and the 2006 MOD14A1 burnt area
product. Ground fire data is falling onto non-fire pixels and there is no evidence of fire burnt
areas within Gwayi State Forest.
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Figure 8 Map showing agreement between ground fire points and the classified MOD14A1
fire product for the year 2009.
Figure 8 is showing an agreement map between ground data and the 2008 MOD14A1 burnt
area product. It can be seen that the entire test set is falling on none fire pixels. This again
shows that there are a poor agreement between the two data sets.
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Figure 9 Map showing agreement between ground fire points and the classified MCD14ML
fire product for the year 2000.
Figure 9 illustrates that no fires were detected in Gwayi State forest by the MCD14Ml fire
product in the year 2000. Appendix 2 shows that there are no fires correctly detected as fire,
six non fire points were correctly predicted as none fire and 6 out of 6 fire points on the
ground were predicted as non-fire. The agreement between the two data sets is poor.
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Figure 10 Map showing agreement between ground fire points and the classified MCD14ML
fire product for the year 2005.
Figure 10 illustrates an overlay of ground fire data onto MCD14ML fire points for the year
2005. Yellow points represent ground fire data and the purple points represent MCD14ML fire
points. It can be observed that, 16 fire points were correctly predicted as fire on the product
data points, 9 fire points on the ground were falsely predicted as non-fire. None of the fires
were ground fire were falsely predicted as none fire on the product and 25 none fire points were
correctly predicted as none fire. The agreement is moderate.
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Figure 11 Map showing agreement between ground fire points and the classified MCD14ML
fire product for the year 2006.
Figure 11 illustrates an overlay of ground data for the year 2006 on an MCD14ML fire product.
It can be observed that, 13 fire points were correctly predicted as fire, 3 none fire points on the
ground were predicted as fire and 16 none fire points were correctly predicted as none fire on
the product. The overall agreement between ground data and the active fire product is high.
24 | P a g e
Figure 12 Map showing agreement between ground fire points and the classified MCD14ML
fire product for the year 2009.
Figure 12 illustrates an agreement of ground data and the MCD14Ml product for the year 2009.
It is evident that, 8 fire points were correctly predicted as fire, none of the ground fires were
falsely predicted as none fire and 12 none fire points on the ground were correctly predicted as
none fire. The agreement is slightly above moderate.
25 | P a g e
Figure 13: Map showing agreement between ground fire data and the MOD14A1 fire product
for the year 2015 (day 1)
Figure 13 illustrates ground fire points in Matusadona for the 18th of September 2015. It can
be seen that there is only one fire scar falling in Gwayi and none of the fire points are overlaying
onto the scar. This shows a poor agreement of the two data sets. Appendix 4).
26 | P a g e
Figure 14: Map showing agreement between ground fire data and the MOD14A1 fire product
for the year 2015 (day 2)
Figure 14 illustrates fire data on an MOD14A1 burnt area product for the 20th of September
and no improvement can be observed. None of the fire points overlay with the burnt area
represented by the red point.
27 | P a g e
Figure 15: Map showing agreement between ground fire data and the MCD14ML fire
product for the year 2015 (day 1)
Figure 15 illustrates fire points on an MCD14ML active fire product for the 18th of September
2015 in Matusadona National Park. It is evident that 6 fire points were correctly predicted as
fires, 11 none fire points were correctly predicted as non-fire, 13 non-fire points were falsely
predicted as and none of the fires were incorrectly classified as none fire. The agreement
between the two data sets is poor.
28 | P a g e
Figure 16: Map showing agreement between ground fire data and the MOD14A1 fire product
for the year 2015
Figure 16 illustrates fire data overlay on an MCD14ML active fire product for the 20th of
September 2015. Observations show an improvement in the agreement of the ground data set
and the active fire product. 10 fire points were correctly classified as fire, 8 none fire points
were falsely classified as fire, none of the fire points were incorrectly classified as none fire
and 11 none fire points were correctly classified as none fire. This again shows a moderate
agreement between the two datasets.
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Chapter 4: Discussion
Findings in this study reveal that MODIS MOD14A1 burnt area fire product has a poor index
of agreement with ground validation data, suggesting that the performance of the product in
detecting burnt areas is no better than random. This finding is consistent with the hypothesis
that increase in spatial resolution decreases the sensors ability to detect fire scars in forest
ecosystems as small fire patches are masked out (Roy and Boschetti 2009). We found that
during the period between 2000 and 2009 detection accuracy of burnt areas was consistently
poor in Gwayi and this result is consistent with the observation in Matusadona National park
in 2015 a different ecosystem dominated by Julbernardia globiflora and Colophospermum
Mopane. Multiple factors may result in poor detection of burnt area fire scars. For example,
errors of sensor data, lack of sensitivity of the burnt area classification algorithms, a course
spatial resolution and limitations of using a global or continental burnt area product on a
smaller location (Roy et al. 2005), (Padilla et al. 2014). However, these findings contradict the
findings of Padilla et al., (Padilla et al. 2014) whose results showed two biomes with higher
accuracy that is, Boreal forest, and Tropical & Subtropical savannah. These findings also differ
from that of a study carried out in Ethiopia by (Molinario et al. 2014) whose results show 90%
probability of fire detection using MODIS MOD14A1. These findings suggest that using a
course spatial resolution, continental burnt area product at a smaller scale may result in poor
detection of fires in Baikiaea plurijuga woodlands than a high resolution sensor with a more
frequent acquisition return interval. This is further emphasised by similar findings in
Matusadona National Park, a different ecosystem from Gwayi State forest.
However, for MODIS MCD14ML with a higher spatial resolution, we consistently find high
accuracy values showing a strong index of agreement with the ground validation data. These
findings suggest that, MODIS MCD14ML active fire product is more accurate in detecting
30 | P a g e
active fires in Baikiaea plurijuga woodlands and its performance is far from random. The
validation results of this study demonstrate that a higher spatial resolution and a more frequent
return interval are important sensor characteristics to explain a higher detection of fires in both
study sites, which harbour Baikiaea plurijuga woodlands. That moderate resolution fire
products are better capable to detect fire in different ecosystems has been demonstrated in
different biomes of the world (Schroeder et al. 2008) Amazonia, (Morisette et al. 2005) South
Africa and (Csiszar et al. 2006) in Northern Eurasia. Consequently, improved sensor
characteristics are more likely to produce relatively frequent omission errors while commission
errors are comparatively rare and active fire data is closely associated to real burnt areas, with
very low commission errors (Hantson et al. 2013).
Relating satellite data on burnt areas can be helpful in understanding the link between fire and
Baikiaea plurijuga ecosystems in the Kalahari sands. This study compared the performance of
two different MODIS fire products using two validation methods that is, kappa and true skill
statistic to account for method limitations in accuracy assessment. Moreover, a comparison of
results from Gwayi and Matusadona reserves using two different data sets ensured us a
conclusive assessment of the two products without data set bias. This type of data or
information is crucial and will assist ecologists to understand a key component of fire regimes
within Baikiaea plurijuga ecosystems which are of economic value in Zimbabwe. Therefore,
eliminating any method limitation was critical. MODIS burnt area products have been validated
in various portions and diverse ecosystems of the globe (Giglio et al. 1999). A number of
publications have been made on the validation of MODIS MOD14 using ASTER, twice in the
Brazilian Amazonia (Morisette et al. 2005);(Schroeder et al. 2008), and once in Siberian boreal
forests (Giglio et al. 2006) and one for southern Africa (Morisette et al. 2005) which discussed
tropical grasslands and savanna biomes. Our findings are novel in view of that, to our
31 | P a g e
knowledge no validation effort has been attempted with regard to Baikiaea plurijuga
woodlands, suggesting that MODIS MCD14ML active fire product may be used to detect and
monitor fire incidences in Baikiaea plurijuga woodlands.
The major limitation to this study is the size of the validation set for all the years which is
relatively small. However, to account for the validation set size, we employed the True Skill
Statistic which is not affected by the size of the validation set and assessed the same products
using a different validation set in a different study site. The availability of data only to the year
2009 limited us to the MODIS MOD14A1 product data archive, however in this perspective
we highlight the MODIS collection 5 MCD45A1 and the new MODIS collection 6 MCD64A1
product, based on the integration of optical and thermal data, (Giglio et al. 2009), which is
anticipated to substantially improve burned area detection specially in dense vegetated areas.
Further investigation may also focus on what effect burned area size has on the sensors ability
to detect post fire scars as this information would be of great interest for burned area algorithm
developers and end-users.
‘
32 | P a g e
Chapter 5: Conclusion
To our knowledge, this study provided the first satellite accuracy assessment evidence that an
increase in spatial resolution may decrease the sensors ability to detect forest fires in Baikiaea
plurijuga woodlands. Increasing the sensors spatial resolution may be necessary to improve
the sensors ability to detect fires in these woodlands, since Baikiaea plurijuga is a hardwood
of economic value, necessitating sustainable conservation of the timber species. This study
concluded that, the global scale burnt area fire product MODIS MOD14A1 is not suitable for
fire detection in Baikiaea plurijuga woodlands, as observed in our findings where kappa and
TSS values constantly remained at 0 implying a poor agreement and a performance no better
than random in both study sites and comparing the four years of assessment in Gwayi. We
suggested that, the poor performance may be a result of the sensors course spatial resolution
and a poor return interval (temporal resolution). However, MODIS MCD14ML active fire
product had high indices of agreement with ground data in both study sites and overall in all
the years under study. Thus, we concluded that the product may suitable for use as an
operational system to detect and map burned areas in an accurate and timely fashion in Baikiaea
plurijuga woodlands.
33 | P a g e
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Appendices
2000 2005
Predict Predict
0 1 Total 0 1 Total
Ground data 0 6 0 6
Ground
data 0 25 0 25
1 6 0 6 Total 1 25 0 25
Total 12 0 12 Total 50 0 50
Appendices 1: Error matrix tables for MODIS MOD14A1 in Gwayi Forest
2000 2005
Predict Predict
1 0 Total 1 0 Total
Ground data 1 0 6 6
Ground
data 1 16 0 16
0 0 6 6 0 9 25 34
Total 0 12 12 Total 25 25 50
2006 2009
Predict Predict
0 1 Total 0 1 Total
Ground data 0 16 0 16
Ground
data 0 12 0 12
Total 1 16 0 16 1 12 0 12
Total 32 0 32 Total 24 0 24
39 | P a g e
2006 2009
Predict Predict
1 0 Total 1 0 Total
Ground data 1 13 0 13
Ground
data 1 8 0 1
0 3 16 19 0 4 12 11
Total 16 16 32 Total 12 12 24
Appendices 2: Error matrix tables for MODIS MCD14ML in Gwayi Forest
2015/09/18 2015/09/20
Predict Predict
1 0 Total 1 0 Total
Ground data 1 6 0 6
Ground
data 1 10 0 10
0 13 11 24 0 8 11 19
Total 19 11 30 Total 18 11 29
Appendices 3: Error matrix tables for MODIS MCD14ML in Matusadona National Park
2015/09/18 2015/09/20
Predict Predict
1 0 Total 1 0 Total
Ground data 1 19 11 30
Ground
data 1 0 0 0
0 0 0 0 0 18 11 29
Total 19 11 30 Total 18 11 29
Appendices 4: Error matrix tables for MODIS MOD14A1 in Matusadona National Park